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Bioinformatics
Brad [email protected]# 628-1956
Web Site: http://www.people.vcu.edu/~bwindle/CoursesClick on Link to MEDC 310 course
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http://www.phc.vcu.edu/310/
Profiling
GeneExpression
ProteinExpression
MiscData
SNPs
Methylation
DrugStructure
ProteinStructure
Cell State
Disease Drug Response
MetaboliticsStructuralGenomic
The term "bioinformatics" is about 15 years old. It covers a variety of data analyses that include:
DNA and protein sequence analysis Biological analysis of drugs, can overlap with chemoinformaticsGeneticsTaxonomyClinical data statisticsGenomic and proteomic research
Bioinformatics is sometimes equated to the term "data mining", which is commonly used in e-business and internet data handling.
Chemoinformatics
Chemoinformatics has a special challenge in that a structure of a compound or drug needs to be quantified. Specific structures are characterized by molecular descriptors useful in Quantitative Structure Activity Relationship (QSAR) modeling. QSAR tells you what about the structure of a drug that makes it do what it does.
Much of this information has implications on what a drug will do in a cell. However, the complexity of a cell makes the reality of what a drug does in the cell deviate significantly from what is anticipated based on chemistry and enzymatic assays. This stresses the need for characterizing drugs based on more biological data.
Analogies for looking for patterns
Looking at patterns in images
A mixture of many patterns
We need to identify individual patterns
There are methods for extracting the patterns from the data
There is also noise tht obscures the patterns
One method for identifying object patterns of interest amidst the noise
Another method for identifying different object patterns of interest amidst the noise
This is what was actually buried in the noise
Questions?
Philosophy of Science
Reductionist Approach (Reductionism)VS
Systems Approach (Systemism)
Reductionist
Systems Approach
Data are analyzed and a hypothesisdeveloped
Experiments are designed and conductedto test the hypothesis, usually involveschanging something in the system
Obervations are made to determine ifthe hypothesis is true or false
Data are analyzed and conclusions made
The hypothesis is either proved true andadvancing to the next stage occurs, orthe hypothesis is proved false and newobervations are made or data is re-analyzed to develop a better hypothesis
Traditional Scientific Methods
Obervations are made with or withoutmaking changes to the system
Technology allows a large amountof observations to be made
Bioinformatics allows analysisof a large amount of data
Bioinformatics allows analysisof a large amount of data
Updated Scientific Methods
Technology allows a large amountof observations to be made
How Does a Cell, or Person Respond to Therapy or a Drug?
Treat 10 people suffering from Disease A with Drug X.• 2 people suffer adverse reactions• 3 exhibit good recovery from disease• 2 exhibit modest recovery from disease• 3 exhibit no sign of recovery from disease
What Factors Cause in Differences Between People?
Genes and their sequenceHealth-wise
• Disease• Health-related Traits• Response to Drugs
What Are the Differences in Genes?
Single nucleotide polymorphisms (SNPs)
SerSerIleAsnGlyGlnLeuArgProAGTTCTATAAATGGCCAGCTTAGACCTTCAAGATATTTACCGGTCGAATCTGGA
SerSerIleHisGlyGlnIleArgProAGTTCTATACATGGCCAGATTAGACCATCAAGATATGTACCGGTCTAATCTGGT
How does a difference in a gene affect drug response?
Transport of the drugMetabolism of the drugInteraction with the drug target
5 Million SNPs
Let’s say there are 10 SNPs that contribute to response to Drug X
Combinatorial approach to identifying SNPs that correlate with drug response
All combinations = 1060
Narrow SNPs down to those within genes to 100,000
Combinations = 1043
Traveling Salesman Problem
SNPs thus far described were inherited, affecting the quality of proteins
What about differences between people that are somatic?
What about quantitative differences in proteins?
Differences in Protein Expression and Gene Expression
20,0000 genes - Genomics
100,000 proteins - Proteomics
Data are analyzed and a hypothesisdeveloped
Experiments are designed and conductedto test the hypothesis, usually involveschanging something in the system
Obervations are made to determine ifthe hypothesis is true or false
Data are analyzed and conclusions made
The hypothesis is either proved true andadvancing to the next stage occurs, orthe hypothesis is proved false and newobervations are made or data is re-analyzed to develop a better hypothesis
Traditional Scientific Methods
Obervations are made with or withoutmaking changes to the system
Technology allows a large amountof observations to be made
Bioinformatics allows analysisof a large amount of data
Bioinformatics allows analysisof a large amount of data
Updated Scientific Methods
Technology allows a large amountof observations to be made
In genomics and proteomics research, the data is extensive and the patterns complex.
The emphasis shifts from asking specific questions or testing hypotheses to trying to filter out the most significant observation the data offers.
Bioinformatics and Data Mining in general use two forms of learning:
Supervised learning is the process of learning by example:Use example patterns with known characteristics to learn and predict characteristics for the unknown
This is essentially the modeling process
Unsupervised learning and Supervised learning
Unsupervised learning is the learning by observation and exploratory data analysis is a general formLet the data reveal prominent patterns and associations, you don’t look forspecific patterns
Exploratory data analysis is used when there is no hypothesis to test, or when there is no specific pattern expected.
This type of analysis shows the most significant pattern or trends within the data; it does not imply biologically or statistical significant.
Cluster analysis is a popular form of exploratory data analysis.
Cluster analysis sorts whatever is being analyzed into clusters with the greatest similarities in trend or pattern. It is a form of non-descriptive statistics and exploratory data analysis.
A dendrogram or tree diagram is used to present the results.
Below is an example of a dendrogram for bacterial species of Escherichia.
New technology= lots of data
Microarray Technology
DNA Microarray
Cell 1’smRNA
Cell 2’smRNA
Pseudo-colored MicroarraySpots
The total intensity for each spot is summed and the values plotted on a scatterplot.
A scatterplot of 2000 points is shown. Each point respresents a gene.
Cluster analysis methods
The most straightforward methods involve calculating the Euclidean (Euclid) distance between two points, for all combinations of points.
Pythagorean Theorem
If we perform cluster analysis on the 2000 points, we can see that we have one giant cluster with a handful of outliers.
Adding Dimensions to Cluster Analysis
The distance calculation would be:
Thus, while we can't visualize more than three dimensions, the computer can perform cluster analysis on as many dimensions imaginable or as processing time allows.
Pearson Correlation Coefficient
Two-fold Cluster Analysis
Gene expression analysis in drug development can involve a large number of genes and a large number of drugs. It is not only important to identify what genes cluster together, but also what drugs cluster . This is done by two-fold cluster analysis.
The genes are arranged and clustered as well as the drugs. The drugs that illicit similar gene expression patterns will cluster. Both clusters can be viewed in a single 2-D dendrogram.
Questions?
Cluster Treeof cell lines
Classifying Cancer
Using supervised learning, models have been developed
Classifying different subsets of cancers that the pathologistcan’t
Predicting response to therapy and patient prognosis
Any kind of data can be explored
Cell response profile
Monks et al. Anti-Cancer Drug Design 12:553 (1997)
Drug clusters correspond to drug targets or mechanisms of action
not necessarily drug structure.
Scherf et al, nature genetics 24:236 (2000)
Exploratory Tools allows us to focus on what most relevant based on the data
And developed relevant hypotheses
For example
Geldanamycin is cytotoxic through inhibition of microtubules
The End
Any Questions?
